Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	83,722
2	South Dakota	73,905
3	Iowa	59,052
4	Wisconsin	56,664
5	Nebraska	50,744
6	Utah	47,975
7	Idaho	46,190
8	Illinois	45,354
9	Mississippi	45,128
10	Tennessee	44,692
11	Alabama	44,424
12	Montana	44,123
13	Arkansas	44,085
14	Louisiana	43,992
15	Florida	41,214
16	Missouri	40,636
17	Kansas	40,452
18	Minnesota	39,654
19	Rhode Island	39,201
20	Nevada	39,200
21	Oklahoma	38,951
22	Wyoming	38,865
23	Georgia	38,569
24	South Carolina	37,972
25	Arizona	37,853
26	Indiana	37,754
27	Texas	37,591
28	Alaska	32,380
29	New Jersey	31,644
30	Kentucky	31,395
31	New Mexico	30,618
32	North Carolina	29,786
33	Delaware	29,579
34	New York	29,041
35	Colorado	28,549
36	Michigan	27,625
37	Maryland	27,510
38	Massachusetts	27,315
39	District of Columbia	26,889
40	California	26,219
41	Ohio	25,502
42	Connecticut	24,863
43	Puerto Rico	23,826
44	Virginia	23,661
45	Pennsylvania	21,015
46	West Virginia	18,781
47	Washington	17,776
48	Oregon	13,485
49	Hawaii	11,737
50	New Hampshire	10,789
51	Maine	6,653
52	Vermont	4,629

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	2,022
2	South Dakota	1,757
3	Iowa	1,364
4	Minnesota	1,287
5	Montana	1,270
6	Wisconsin	1,163
7	Wyoming	1,160
8	Utah	1,057
9	Indiana	1,021
10	Illinois	969
11	Nebraska	966
12	Alaska	910
13	Colorado	876
14	Oklahoma	794
15	Missouri	773
16	Kansas	726
17	Idaho	684
18	Ohio	674
19	Tennessee	670
20	Nevada	582
21	Kentucky	565
22	Michigan	564
23	Arkansas	556
24	New Mexico	544
25	West Virginia	513
26	Alabama	488
27	New Jersey	461
28	Delaware	430
29	Louisiana	428
30	Mississippi	408
31	Arizona	406
32	Massachusetts	391
33	Pennsylvania	387
34	Texas	337
35	Florida	334
36	Maryland	331
37	South Carolina	324
38	New Hampshire	294
39	North Carolina	269
40	Connecticut	256
41	Washington	249
42	New York	247
43	Georgia	243
44	Rhode Island	240
45	Oregon	237
46	California	226
47	District of Columbia	221
48	Puerto Rico	211
49	Virginia	153
50	Maine	136
51	Vermont	127
52	Hawaii	50

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,865
2	New York	1,722
3	Massachusetts	1,498
4	Connecticut	1,328
5	Louisiana	1,319
6	Mississippi	1,190
7	Rhode Island	1,183
8	North Dakota	973
9	District of Columbia	935
10	Illinois	881
11	Arizona	865
12	Michigan	838
13	Georgia	822
14	Florida	815
15	South Carolina	798
16	Delaware	755
17	Pennsylvania	733
18	Indiana	729
19	South Dakota	727
20	Arkansas	723
21	Maryland	711
22	Texas	693
23	Alabama	662
24	Iowa	630
25	Nevada	621
26	New Mexico	579
27	Missouri	564
28	Tennessee	564
29	Minnesota	525
30	Montana	491
31	Ohio	489
32	Wisconsin	473
33	California	462
34	North Carolina	460
35	Colorado	446
36	Virginia	445
37	Kansas	431
38	Idaho	426
39	Nebraska	408
40	Kentucky	389
41	Oklahoma	386
42	New Hampshire	366
43	Washington	346
44	West Virginia	324
45	Puerto Rico	292
46	Wyoming	248
47	Utah	223
48	Oregon	180
49	Hawaii	156
50	Alaska	125
51	Maine	122
52	Vermont	94

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	29
2	North Dakota	17
3	Montana	14
4	Wyoming	9
5	Illinois	8
6	Wisconsin	7
7	Michigan	6
8	Minnesota	6
9	New Mexico	6
10	Tennessee	5
11	West Virginia	5
12	Arkansas	4
13	Indiana	4
14	Iowa	4
15	Kansas	4
16	Massachusetts	4
17	Texas	4
18	Colorado	3
19	Mississippi	3
20	Nebraska	3
21	Nevada	3
22	North Carolina	3
23	Oklahoma	3
24	Utah	3
25	Alabama	2
26	Arizona	2
27	Florida	2
28	Georgia	2
29	Kentucky	2
30	Louisiana	2
31	Maryland	2
32	New Jersey	2
33	Pennsylvania	2
34	Puerto Rico	2
35	California	1
36	Connecticut	1
37	Delaware	1
38	District of Columbia	1
39	Idaho	1
40	Maine	1
41	Missouri	1
42	New York	1
43	Ohio	1
44	Oregon	1
45	Rhode Island	1
46	South Carolina	1
47	Virginia	1
48	Alaska	0
49	Hawaii	0
50	New Hampshire	0
51	Vermont	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Norton	Kansas	185,600	1	99
Lincoln	Arkansas	181,511	2	99
Chattahoochee	Georgia	177,776	3	99
Bon Homme	South Dakota	175,772	4	99
Buffalo	South Dakota	168,705	5	99
Davidson	Tennessee	57,399	410	86
Richland	South Carolina	45,461	835	73
York	South Carolina	29,080	1857	40
Orange	California	21,236	2369	24
Pierce	Washington	15,971	2632	16

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	683	1073	65
Davidson	Tennessee	550	1355	56
Orange	California	481	1526	51
York	South Carolina	431	1670	46
Pierce	Washington	315	2007	36

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.